SmokeSeer: 多视角视频序列中烟雾去除与场景重建的3D高斯点云技术
SmokeSeer: 3D Gaussian Splatting for Smoke Removal and Scene Reconstruction
AI总结：
SmokeSeer通过3D高斯点云技术实现多视角视频中烟雾去除与场景重建，支持多种烟雾密度和时间变化烟雾的处理。
摘要：
现实世界中的烟雾会严重降低图像质量并阻碍可视性。最近的图像修复方法要么依赖于易产生幻觉的数据驱动先验知识，要么局限于静态低密度烟雾。我们引入了SmokeSeer，一种用于同时从多视角视频序列中进行3D场景重建和烟雾去除的方法。我们的方法利用热成像和RGB图像，借助热成像中减少的散射特性穿透烟雾。我们基于3D高斯点云技术融合两种图像模态的信息，并将场景分解为烟雾和非烟雾组成部分。与以往工作不同，SmokeSeer能够处理各种密度的烟雾，并适应时间变化的烟雾。我们在合成数据和一个新的现实世界烟雾数据集上验证了我们的方法，该数据集包含RGB和热成像图像。我们还提供了项目网站上的开源实现和数据。
英文摘要：
Smoke in real-world scenes can severely degrade image quality and hamper visibility. Recent image restoration methods either rely on data-driven priors that are susceptible to hallucinations, or are limited to static low-density smoke. We introduce SmokeSeer, a method for simultaneous 3D scene reconstruction and smoke removal from multi-view video sequences. Our method uses thermal and RGB images, leveraging the reduced scattering in thermal images to see through smoke. We build upon 3D Gaussian splatting to fuse information from the two image modalities, and decompose the scene into smoke and non-smoke components. Unlike prior work, SmokeSeer handles a broad range of smoke densities and adapts to temporally varying smoke. We validate our method on synthetic data and a new real-world smoke dataset with RGB and thermal images. We provide an open-source implementation and data on the project website.
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